Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations101230332
Missing cells2744044
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 GiB
Average record size in memory80.0 B

Variable types

Numeric6
Categorical3
Boolean1

Alerts

answered_correctly is highly overall correlated with content_id and 2 other fieldsHigh correlation
content_id is highly overall correlated with answered_correctly and 1 other fieldsHigh correlation
content_type_id is highly overall correlated with answered_correctly and 3 other fieldsHigh correlation
prior_question_elapsed_time is highly overall correlated with content_type_idHigh correlation
row_id is highly overall correlated with user_idHigh correlation
task_container_id is highly overall correlated with timestampHigh correlation
timestamp is highly overall correlated with task_container_idHigh correlation
user_answer is highly overall correlated with answered_correctly and 1 other fieldsHigh correlation
user_id is highly overall correlated with row_idHigh correlation
content_type_id is highly imbalanced (86.2%) Imbalance
prior_question_elapsed_time has 2351538 (2.3%) missing values Missing
row_id is uniformly distributed Uniform
row_id has unique values Unique

Reproduction

Analysis started2024-11-25 02:02:20.779455
Analysis finished2024-11-25 02:14:31.209640
Duration12 minutes and 10.43 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

row_id
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct101230332
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50615166
Minimum0
Maximum1.0123033 × 108
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size772.3 MiB
2024-11-25T05:14:32.575968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5061516.6
Q125307583
median50615166
Q375922748
95-th percentile96168814
Maximum1.0123033 × 108
Range1.0123033 × 108
Interquartile range (IQR)50615166

Descriptive statistics

Standard deviation29222680
Coefficient of variation (CV)0.57735028
Kurtosis-1.2
Mean50615166
Median Absolute Deviation (MAD)25307583
Skewness-1.1536043 × 10-15
Sum5.12379 × 1015
Variance8.5396502 × 1014
MonotonicityStrictly increasing
2024-11-25T05:14:32.624823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101230331 1
 
< 0.1%
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
Other values (101230322) 101230322
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
101230331 1
< 0.1%
101230330 1
< 0.1%
101230329 1
< 0.1%
101230328 1
< 0.1%
101230327 1
< 0.1%
101230326 1
< 0.1%
101230325 1
< 0.1%
101230324 1
< 0.1%
101230323 1
< 0.1%
101230322 1
< 0.1%

timestamp
Real number (ℝ)

High correlation 

Distinct72821015
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7036437 × 109
Minimum0
Maximum8.7425772 × 1010
Zeros396417
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size772.3 MiB
2024-11-25T05:14:33.681965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile484671
Q15.2434356 × 108
median2.6742338 × 109
Q39.9245506 × 109
95-th percentile3.3017101 × 1010
Maximum8.7425772 × 1010
Range8.7425772 × 1010
Interquartile range (IQR)9.400207 × 109

Descriptive statistics

Standard deviation1.1592655 × 1010
Coefficient of variation (CV)1.5048276
Kurtosis6.436403
Mean7.7036437 × 109
Median Absolute Deviation (MAD)2.5783195 × 109
Skewness2.3881907
Sum7.798424 × 1017
Variance1.3438966 × 1020
MonotonicityNot monotonic
2024-11-25T05:14:33.726082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 396417
 
0.4%
24163 48
 
< 0.1%
24220 48
 
< 0.1%
24111 46
 
< 0.1%
25010 46
 
< 0.1%
23587 46
 
< 0.1%
24755 46
 
< 0.1%
24072 46
 
< 0.1%
24702 46
 
< 0.1%
23823 46
 
< 0.1%
Other values (72821005) 100833497
99.6%
ValueCountFrequency (%)
0 396417
0.4%
676 1
 
< 0.1%
912 1
 
< 0.1%
1399 1
 
< 0.1%
1752 1
 
< 0.1%
1805 1
 
< 0.1%
1892 1
 
< 0.1%
1920 1
 
< 0.1%
2017 1
 
< 0.1%
2020 1
 
< 0.1%
ValueCountFrequency (%)
8.742577205 × 10101
< 0.1%
8.71933551 × 10101
< 0.1%
8.719333208 × 10101
< 0.1%
8.719327905 × 10101
< 0.1%
8.719307657 × 10101
< 0.1%
8.719289875 × 10101
< 0.1%
8.719282239 × 10101
< 0.1%
8.719276208 × 10101
< 0.1%
8.719274019 × 10101
< 0.1%
8.719272534 × 10101
< 0.1%

user_id
Real number (ℝ)

High correlation 

Distinct393656
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0767324 × 109
Minimum115
Maximum2.1474829 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size772.3 MiB
2024-11-25T05:14:33.773469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum115
5-th percentile1.0845148 × 108
Q15.4081156 × 108
median1.0717811 × 109
Q31.6157417 × 109
95-th percentile2.0399148 × 109
Maximum2.1474829 × 109
Range2.1474828 × 109
Interquartile range (IQR)1.0749301 × 109

Descriptive statistics

Standard deviation6.1971635 × 108
Coefficient of variation (CV)0.57555279
Kurtosis-1.1998438
Mean1.0767324 × 109
Median Absolute Deviation (MAD)5.374965 × 108
Skewness0.00097151008
Sum1.0899798 × 1017
Variance3.8404835 × 1017
MonotonicityIncreasing
2024-11-25T05:14:33.820014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
801103753 17917
 
< 0.1%
1478712595 16914
 
< 0.1%
1842816145 16851
 
< 0.1%
455973631 16789
 
< 0.1%
1660941992 16777
 
< 0.1%
1743444187 16654
 
< 0.1%
2146130037 16384
 
< 0.1%
1047202059 16348
 
< 0.1%
1615528747 16146
 
< 0.1%
338684437 15963
 
< 0.1%
Other values (393646) 101063589
99.8%
ValueCountFrequency (%)
115 46
 
< 0.1%
124 30
 
< 0.1%
2746 20
 
< 0.1%
5382 128
 
< 0.1%
8623 112
 
< 0.1%
8701 17
 
< 0.1%
12741 271
 
< 0.1%
13134 1250
 
< 0.1%
24418 6464
< 0.1%
24600 50
 
< 0.1%
ValueCountFrequency (%)
2147482888 27
 
< 0.1%
2147482216 280
 
< 0.1%
2147481750 50
 
< 0.1%
2147470777 758
< 0.1%
2147470770 228
 
< 0.1%
2147469944 276
 
< 0.1%
2147464207 50
 
< 0.1%
2147463192 17
 
< 0.1%
2147457494 39
 
< 0.1%
2147455775 30
 
< 0.1%

content_id
Real number (ℝ)

High correlation 

Distinct13782
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5219.6048
Minimum0
Maximum32736
Zeros6903
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size772.3 MiB
2024-11-25T05:14:33.863497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile325
Q12063
median5026
Q37425
95-th percentile10719
Maximum32736
Range32736
Interquartile range (IQR)5362

Descriptive statistics

Standard deviation3866.3589
Coefficient of variation (CV)0.74073788
Kurtosis6.9552696
Mean5219.6048
Median Absolute Deviation (MAD)2787
Skewness1.5647616
Sum5.2838232 × 1011
Variance14948731
MonotonicityNot monotonic
2024-11-25T05:14:33.910867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6116 213605
 
0.2%
6173 202106
 
0.2%
4120 199372
 
0.2%
175 195861
 
0.2%
7876 190170
 
0.2%
7900 180858
 
0.2%
2063 176043
 
0.2%
2065 176043
 
0.2%
2064 176043
 
0.2%
4492 173769
 
0.2%
Other values (13772) 99346462
98.1%
ValueCountFrequency (%)
0 6903
 
< 0.1%
1 7398
 
< 0.1%
2 44905
< 0.1%
3 22973
< 0.1%
4 31736
< 0.1%
5 9727
 
< 0.1%
6 56707
0.1%
7 16585
 
< 0.1%
8 8535
 
< 0.1%
9 47346
< 0.1%
ValueCountFrequency (%)
32736 8013
 
< 0.1%
32625 8031
 
< 0.1%
32604 4
 
< 0.1%
32570 2910
 
< 0.1%
32535 3666
 
< 0.1%
32491 1655
 
< 0.1%
32312 20943
< 0.1%
32264 72
 
< 0.1%
32175 5455
 
< 0.1%
32168 9820
< 0.1%

content_type_id
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size772.3 MiB
0
99271300 
1
 
1959032

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101230332
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 99271300
98.1%
1 1959032
 
1.9%

Length

2024-11-25T05:14:33.953850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T05:14:33.989635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 99271300
98.1%
1 1959032
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 99271300
98.1%
1 1959032
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101230332
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 99271300
98.1%
1 1959032
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 101230332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 99271300
98.1%
1 1959032
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101230332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 99271300
98.1%
1 1959032
 
1.9%

task_container_id
Real number (ℝ)

High correlation 

Distinct10000
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean904.06237
Minimum0
Maximum9999
Zeros395990
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size772.3 MiB
2024-11-25T05:14:34.026058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q1104
median382
Q31094
95-th percentile3697
Maximum9999
Range9999
Interquartile range (IQR)990

Descriptive statistics

Standard deviation1358.3022
Coefficient of variation (CV)1.502443
Kurtosis10.150178
Mean904.06237
Median Absolute Deviation (MAD)337
Skewness2.8782535
Sum9.1518533 × 1010
Variance1844984.8
MonotonicityNot monotonic
2024-11-25T05:14:34.070828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 804285
 
0.8%
15 798539
 
0.8%
4 692079
 
0.7%
5 690051
 
0.7%
6 688813
 
0.7%
7 684275
 
0.7%
11 403521
 
0.4%
10 400660
 
0.4%
8 400019
 
0.4%
9 399641
 
0.4%
Other values (9990) 95268449
94.1%
ValueCountFrequency (%)
0 395990
0.4%
1 395934
0.4%
2 395595
0.4%
3 395341
0.4%
4 692079
0.7%
5 690051
0.7%
6 688813
0.7%
7 684275
0.7%
8 400019
0.4%
9 399641
0.4%
ValueCountFrequency (%)
9999 174
< 0.1%
9998 185
< 0.1%
9997 185
< 0.1%
9996 184
< 0.1%
9995 191
< 0.1%
9994 188
< 0.1%
9993 178
< 0.1%
9992 180
< 0.1%
9991 191
< 0.1%
9990 179
< 0.1%

user_answer
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size772.3 MiB
0
28186489 
1
26990007 
3
26084784 
2
18010020 
-1
 
1959032

Length

Max length2
Median length1
Mean length1.0193522
Min length1

Characters and Unicode

Total characters103189364
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 28186489
27.8%
1 26990007
26.7%
3 26084784
25.8%
2 18010020
17.8%
-1 1959032
 
1.9%

Length

2024-11-25T05:14:34.112020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T05:14:34.146236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 28949039
28.6%
0 28186489
27.8%
3 26084784
25.8%
2 18010020
17.8%

Most occurring characters

ValueCountFrequency (%)
1 28949039
28.1%
0 28186489
27.3%
3 26084784
25.3%
2 18010020
17.5%
- 1959032
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101230332
98.1%
Dash Punctuation 1959032
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28949039
28.6%
0 28186489
27.8%
3 26084784
25.8%
2 18010020
17.8%
Dash Punctuation
ValueCountFrequency (%)
- 1959032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103189364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28949039
28.1%
0 28186489
27.3%
3 26084784
25.3%
2 18010020
17.5%
- 1959032
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103189364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28949039
28.1%
0 28186489
27.3%
3 26084784
25.3%
2 18010020
17.5%
- 1959032
 
1.9%

answered_correctly
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size772.3 MiB
1
65244627 
0
34026673 
-1
 
1959032

Length

Max length2
Median length1
Mean length1.0193522
Min length1

Characters and Unicode

Total characters103189364
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 65244627
64.5%
0 34026673
33.6%
-1 1959032
 
1.9%

Length

2024-11-25T05:14:34.185514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T05:14:34.217527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 67203659
66.4%
0 34026673
33.6%

Most occurring characters

ValueCountFrequency (%)
1 67203659
65.1%
0 34026673
33.0%
- 1959032
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101230332
98.1%
Dash Punctuation 1959032
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 67203659
66.4%
0 34026673
33.6%
Dash Punctuation
ValueCountFrequency (%)
- 1959032
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103189364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 67203659
65.1%
0 34026673
33.0%
- 1959032
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103189364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 67203659
65.1%
0 34026673
33.0%
- 1959032
 
1.9%

prior_question_elapsed_time
Real number (ℝ)

High correlation  Missing 

Distinct3258
Distinct (%)< 0.1%
Missing2351538
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean25423.81
Minimum0
Maximum300000
Zeros194817
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size772.3 MiB
2024-11-25T05:14:34.253807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6000
Q116000
median21000
Q329666
95-th percentile58000
Maximum300000
Range300000
Interquartile range (IQR)13666

Descriptive statistics

Standard deviation19948.147
Coefficient of variation (CV)0.7846246
Kurtosis45.146464
Mean25423.81
Median Absolute Deviation (MAD)6000
Skewness4.8233272
Sum2.5138757 × 1012
Variance3.9792856 × 108
MonotonicityNot monotonic
2024-11-25T05:14:34.297457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17000 5154588
 
5.1%
16000 4752104
 
4.7%
18000 4670915
 
4.6%
19000 4017031
 
4.0%
15000 3640279
 
3.6%
20000 3597394
 
3.6%
21000 3325947
 
3.3%
22000 3041894
 
3.0%
23000 2677660
 
2.6%
14000 2634224
 
2.6%
Other values (3248) 61366758
60.6%
(Missing) 2351538
 
2.3%
ValueCountFrequency (%)
0 194817
0.2%
200 183
 
< 0.1%
250 417
 
< 0.1%
333 94610
0.1%
400 95
 
< 0.1%
500 2810
 
< 0.1%
600 973
 
< 0.1%
666 117774
0.1%
667 4694
 
< 0.1%
750 25590
 
< 0.1%
ValueCountFrequency (%)
300000 71896
0.1%
299800 13
 
< 0.1%
299750 13
 
< 0.1%
299666 1
 
< 0.1%
299600 3
 
< 0.1%
299500 5
 
< 0.1%
299400 4
 
< 0.1%
299333 6
 
< 0.1%
299250 6
 
< 0.1%
299200 2
 
< 0.1%

prior_question_had_explanation
Boolean

Distinct2
Distinct (%)< 0.1%
Missing392506
Missing (%)0.4%
Memory size772.3 MiB
True
89685560 
False
11152266 
(Missing)
 
392506
ValueCountFrequency (%)
True 89685560
88.6%
False 11152266
 
11.0%
(Missing) 392506
 
0.4%
2024-11-25T05:14:34.333756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2024-11-25T05:12:35.488249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:10:52.203141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:12.199090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:32.065007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:52.219199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:13.927723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:39.522991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:10:55.438002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:15.290547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:35.253960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:55.827456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:17.153987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:43.547730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:10:58.636601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:18.544063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:38.311427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:59.459549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:20.390082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:47.520035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:01.963133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:21.855219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:41.636047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:02.987299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:23.729438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:51.532767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:05.141737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:25.082758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:44.818896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:06.610797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:27.674704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:55.208553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:08.918519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:28.870756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:11:48.559563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:10.691744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T05:12:31.435697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-25T05:14:34.858103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
answered_correctlycontent_idcontent_type_idprior_question_elapsed_timeprior_question_had_explanationrow_idtask_container_idtimestampuser_answeruser_id
answered_correctly1.0000.5281.0000.0200.4110.0020.0430.0200.7070.002
content_id0.5281.0000.7430.1770.3000.0010.0570.0500.3770.001
content_type_id1.0000.7431.0001.0000.3990.0010.0250.0031.0000.001
prior_question_elapsed_time0.0200.1771.0001.0000.022-0.000-0.0020.0110.033-0.000
prior_question_had_explanation0.4110.3000.3990.0221.0000.0050.1480.1390.4000.005
row_id0.0020.0010.001-0.0000.0051.0000.0020.0010.0011.000
task_container_id0.0430.0570.025-0.0020.1480.0021.0000.6980.0130.002
timestamp0.0200.0500.0030.0110.1390.0010.6981.0000.0030.001
user_answer0.7070.3771.0000.0330.4000.0010.0130.0031.0000.001
user_id0.0020.0010.001-0.0000.0051.0000.0020.0010.0011.000

Missing values

2024-11-25T05:12:56.549616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-25T05:13:18.669109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-25T05:14:04.941673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

row_idtimestampuser_idcontent_idcontent_type_idtask_container_iduser_answeranswered_correctlyprior_question_elapsed_timeprior_question_had_explanation
00011556920131NaNNaN
11569431155716022137000.0False
22118363115128000155000.0False
331311671157860030119000.0False
441379651157922041111000.0False
5515706311515605215000.0False
6617609211551060117000.0False
7719419011550073117000.0False
882124631157896082116000.0False
992309831157863090116000.0False
row_idtimestampuser_idcontent_idcontent_type_idtask_container_iduser_answeranswered_correctlyprior_question_elapsed_timeprior_question_had_explanation
101230322101230322428415725214748288850050171030000.0True
10123032310123032342844960921474828888711018209000.0True
101230324101230324428466169214748288855910193126000.0True
101230325101230325428517313214748288861280201111000.0True
101230326101230326428542872214748288862020212144000.0True
101230327101230327428564420214748288835860220118000.0True
101230328101230328428585000214748288863410233114000.0True
101230329101230329428613475214748288842120243114000.0True
101230330101230330428649406214748288863430251022000.0True
101230331101230331428692118214748288879950263129000.0True